The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple 'for-loop' construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

Tuning surface strain is a powerful strategy for tailoring the reactivity of metal catalysts. Traditionally, surface strain is imposed by external stress from a heterogeneous substrate, but the effect is often obscured by interfacial reconstructions and nanocatalyst geometries. Here, we report on a strategy to resolve these problems by exploiting intrinsic surface stresses in two-dimensional transition metal nanosheets. Density functional theory calculations indicate that attractive interactions between surface atoms lead to tensile surface stresses that exert a pressure on the order of 105atmospheres on the surface atoms and impart up to 10% compressive strain, with the exact magnitude inversely proportional to the nanosheet thickness. Atomic-level control of thickness thus enables generation and fine-tuning of intrinsic strain to optimize catalytic reactivity, which was confirmed experimentally on Pd(110) nanosheets for the oxygen reduction and hydrogen evolution reactions, with activity enhancements that were more than an order of magnitude greater than those of their nanoparticle counterparts.

Heterogeneous catalysts constitute a crucial component of many industrial processes, and to gain an understanding of the atomicscale features of such catalysts, ab initio density functional theory is widely employed. Recently, growing computational power has permitted the extension of such studies to complex reaction networks involving either high adsorbate coverages or multidentate adsorbates, which bind to the surface through multiple atoms. Describing all possible adsorbate configurations for such systems, however, is often not possible based on chemical intuition alone. To systematically treat such complexities, we present a generalized Python-based graph theory approach to convert atomic scale models into undirected graph representations. These representations, when combined with workflows such as evolutionary algorithms, can systematically generate high coverage adsorbate models and classify unique minimum energy multidentate adsorbate configurations for surfaces of low symmetry, including multi-elemental alloy surfaces, steps, and kinks. Two case studies are presented which demonstrate these capabilities; first, an analysis of a coverage-dependent phase diagram of absorbate NO on the Pt 3 Sn(111) terrace surface, and second, an investigation of adsorption energies, together with identifying unique minimum energy configurations, for the reaction intermediate propyne (CHCCH 3 *) adsorbed on a PdIn(021) step surface. The evolutionary algorithm approach reproduces high coverage configurations of NO on Pt 3 Sn(111) using only 15% of the number of simulations required for a brute force approach. Furthermore, the screening of potentially hundreds of multidentate adsorbates is shown to be possible without human intervention. The strategy presented is quite general and can be applied to a spectrum of complex atomic systems.

A variety of anion–π complexes of thiocyanate showed common trends in changes of thermodynamic, spectral and structural features with variations in redox- and surface electrostatic potentials of the π-acceptor.

Halogen bonding between two negatively charged species, tetraiodo‐p‐benzoquinone anion radicals (I4Q−.) and iodide anions, was observed and characterized for the first time. X‐ray structural and EPR/UV–Vis spectral studies revealed that the anion–anion bonding led to the formation of crystals comprising 2D layers of I4Q−. anion radicals linked by iodides and separated by Et4N+ counter‐ions. Computational analysis suggested that the seemingly antielectrostatic halogen bonds in these systems were formed via a combination of several factors. First, an attenuation of the interionic repulsion by the solvent facilitated close approach of the anions leading to their mutual polarization. This resulted in the appearance of positively charged areas (σ‐holes) on the surface of the iodine substituents in I4Q−. responsible for the attractive interaction. Finally, the solid‐state associations were also stabilized by multicenter (4:4) halogen bonding between I4Q−. and iodide.